CO2 Capture Using Covalent Organic Framworks and the Formation of Methanol in the Presence of Metal Catalyst and Metal Sulfides (CO2 capture)

Started

Fossil Fuels: Oil Gas and Coal(CO2 Capture and Storage, CO2 capture/separation)

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Chemistry)
PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics)

Not Cross-cutting

Dr K Jelfs
Chemistry
Imperial College London

Standard

EPSRC

04 February 2025

03 February 2027

24 months

£192,297

Unknown

London

UKRI MSCA

Dr K Jelfs, Chemistry, Imperial College London

Dr M Mansoori Kermani, Imperial College London

Global warming is a serious worldwide threat with a significant impact on ecosystems, and CO2 emission is intimately tied to this threat. This project aims to design a novel integrated research plan for CO2 capture using Covalent Organic Frameworks (COFs) and the formation of methanol in the presence of metal catalyst and metal sulfides through a series of innovative investigations. Our objectives are as follows. Objective I: We use density functional theory to predict how we can increase the CO2 uptake capacity of COF by exploring its structure. Objective II: We test the capacity of COFs to uptake CO2 in a multivariable environment by molecular dynamics simulation followed by active learning to produce an effective search algorithm for CO2 capture. Objective III: We use density functional theory to study the mechanism of CO2 reduction to methanol by Pt and by embedding a metal sulfide defect into the COF. The research and innovation objectives of the project benefit from the strong connections between its components, which have been carefully designed for effective measurement and verification. It contributes to the field by deepening our understanding of the functionality of COFs, finding an efficient search algorithm for optimal operation condition for CO2 uptake by COFs, and exploring novel pathways for methanol production. This proposal involves the use of density functional theory, molecular dynamics simulation and active learning as a subset of machine learning to predict materials properties for the use of COFs to CO2 uptake and methanol production. This ambitious effort aims to advance beyond the current state of knowledge by seamlessly integrating various computational chemistry methods. This approach will comprehensively address the potential of COFs based on i) the previous experiences of the researcher and the host and ii) transferring the knowledge and skills between them

25/06/25